Demystifying the Systems, Memory, Reasoning, and Workflows Powering the Next Generation of AI

If you're a prompt engineer, you're not just writing clever inputs. You're shaping intelligent behavior. In the era of Agentic AI, success hinges on understanding the building blocks of these autonomous systems—how they reason, remember, collaborate, and act.

This guide breaks down 50+ essential Agentic AI terms and concepts you need to know to navigate, build, and optimize AI systems like a pro. Whether you're crafting workflows, training vertical agents, or building hybrid systems, this glossary will help you communicate clearly, collaborate smarter, and create with confidence.

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🧠 Foundation: Models and Architectures

1. LLM (Large Language Model)

Trained on massive datasets to generate human-like text, code, and more.

2. LRM (Large Reasoning Model)

Built for multi-step, logic-heavy tasks that require structured thinking.

3. Hybrid Models

Systems that switch between fast heuristics and deep reasoning depending on task complexity.

4. MoE (Mixture of Experts)

A model architecture where only the most relevant sub-models ("experts") are activated during inference.

🤖 The Agentic Stack: Agents and Autonomy

5. Agentic AI

An AI that can act autonomously—deciding what, when, and how to do something.

6. Agents

Self-directed units capable of choosing tools, retrieving knowledge, and executing actions.

7. Multi-Agent Systems

Multiple agents collaborating on a task—like an AI "team" with different roles.

8. Vertical Agents

Specialized agents tailored for domains like healthcare, finance, or legal.

9. Orchestrator

A controller that assigns roles and tasks to multiple agents, managing execution order and logic.

🧠 Memory Models: How Agents Remember

10. Agent Memory

The backbone of an agent’s performance—its ability to store and recall experiences.

11. Short-Term Memory

Session-based memory that exists temporarily for immediate context.

12. Long-Term Memory

Persistent memory that spans across sessions and tasks. Used in tools like ChatGPT with memory.

13. Procedural Memory

The agent's ability to remember how to perform a process or follow a learned sequence.

14. Reflection

Agents self-assess previous actions to improve future decisions.

15. Self-Healing

Autonomous detection and correction of errors, without human involvement.

🔄 Reasoning and Planning

16. Reasoning

The logic used by agents to evaluate input and plan responses.

17. Chain of Thought (CoT)

Breaking down decisions step-by-step, making model reasoning transparent and reliable.

18. ReAct (Reason + Act)

Combining CoT with tool usage—think first, act next.

19. Chaining

Breaking complex tasks into smaller subtasks and solving them sequentially.

20. Overthinking

When agents consume excessive tokens or steps unnecessarily. Can be limited by runtime parameters.

21. Test-Time Scaling

Dynamic adjustment of an agent's cognitive depth based on the complexity of the task.

🛠️ Tools, Functions, and Integration

22. Tools

External services the AI can call, like APIs, databases, or search engines.

23. Function Calling

Structured instructions that let agents call specific functions when needed.

24. Structured Output

AI responses in JSON, XML, or other predictable formats—crucial for reliable downstream use.

25. MCP (Model Context Protocol)

A standardized way for agents to interact with tools and data—like USB for AI.

📚 Knowledge-Augmented Reasoning

26. RAG (Retrieval-Augmented Generation)

Enhancing generation by retrieving external documents during inference.

27. Agentic RAG

An intelligent agent decides when to search and how to use retrieved information.

⚙️ Workflows and Execution Logic

28. Workflows

Predefined sequences that dictate how agents interact with models and tools.

29. Routing

Directing parts of a task to the most suitable model or agent.

30. Design Patterns

Reusable solutions to common problems in building agentic systems (e.g., planner-executor).

🧪 Training, Tuning, and Optimization

31. Continual Pretraining

Updating models with fresh, unlabeled data beyond their initial training.

32. Supervised Fine-Tuning

Improving performance with labeled data on specific tasks.

33. RL (Reinforcement Learning)

Training through trial and error—agents learn from rewards and penalties.

34. RLHF (Reinforcement Learning from Human Feedback)

A hybrid method using human judgment to guide agent behavior.

35. Distillation

Transferring knowledge from a large model to a smaller one by mimicking behavior.

36. Post-Training

Any tuning or refinement after a model is initially trained.

37. Alignment

Ensuring model outputs reflect ethical, accurate, and human-aligned behavior.

🧑‍🤝‍🧑 Human & AI Collaboration

38. HITL (Human-in-the-Loop)

Keeping humans in the decision-making loop to validate or intervene.

39. LLM Judge

An LLM tasked with evaluating other model outputs for correctness or quality.

🧠 Advanced Thinking Models

40. Cognitive Architecture

The blueprint for how an AI system processes input, stores memory, and produces actions.

41. Meta-Cognition

An agent's awareness of its own thinking process—used in advanced reflection and planning.

42. Planner-Executor Pattern

One AI plans the steps; another executes them—common in structured agentic workflows.

🧩 Bonus Terms for Prompt Engineers

43. Prompt Chaining

Linking a series of prompts and responses to guide agents through complex logic.

44. Prompt Templates

Reusable prompt formats that ensure consistency and control over responses.

45. System Prompts

Hidden instructions that guide an AI’s persona, tone, or behavior in multi-turn conversations.

46. Context Window

The token limit an LLM can "remember" in a single session (e.g., 8K, 32K, 1M tokens).

47. Few-Shot Learning

A method where an AI model learns to generalize from just a few examples within the prompt itself. Often used in prompt engineering to guide agent behavior quickly without retraining.

48. Toolformer

A special type of model trained to decide when and how to call external tools like calculators, web search, or databases — crucial for agents that interact with dynamic systems.

49. Autonomous Task Decomposition

The ability of an AI agent to break down complex tasks into smaller, manageable subtasks automatically, without human help.

50. Skill Acquisition

In Agentic AI, this refers to an agent’s ability to learn new capabilities (tools, functions, patterns) over time through interaction or retraining.

51. Dynamic Prompting

A technique where the prompt changes in real time based on the task context, environment, or memory. Especially useful for agents working in open-ended or evolving situations.

52. Constitutional AI

A method where AI behavior is guided by a set of principles or “rules” (like a constitution), used in place of or alongside human feedback to align the model’s responses.

🎯 Final Thoughts

These terms aren’t just buzzwords—they're the grammar of the new AI era. If you’re a prompt engineer, system designer, or even just curious about building intelligent agents, mastering this vocabulary will supercharge your ability to design, debug, and collaborate.

🧠 Pro Tip: Don’t just memorize—experiment. Use these concepts while building your own agents or automations.

📌 Save this guide and revisit it often as you grow in your AI journey. Use the Coupon code QPT to get a discount on my AI Course

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